November 2, 2018

There's no technology without science, there's no science without data

In today's world, technology is far more popular than science, and this is an astonishing fact taking into consideration that one cannot exist without the other. It's not a chicken and egg situation of course, but in most young consumers of technology, and even some of the most savvy ones, technology seems to have displaced science of its "cool" status. Let's look at some stats.


Well, from the above we can see "Science" is typically more searched for, than "Technology". But this is not a good metric for two reasons:
1) People do not search for "Scientific" themes by putting the "Science" word on it. A quick look at scientific trends will reveal some specific trends like "Biotechnology", or "Global Warming".
2) For the same reasons as above people do not search for "Technology" but more the trends. Technology is so pervasive these days that we really need to split between the consumer technology and the enterprise technology. In consumer technology the smartphone is king and the obsession is still very much present in the market, so let's go with the two more popular models: "Samsung Galaxy" and "Apple iPhone". In enterprise computing the two main trends are "Blockchain" and "Artificial Intelligence".

So let's see how some of these trends stack against each other in Google Trends. First the two science trends we identified above: "Biotechnology" versus "Global Warming".


Very close! Global Warming had a sudden rise in the last month, but all in all very much comparable. So we can choose either one to stack against the technology trends.
Now let's look at both technology trends classes. First consumer technology.


Not a surprise that the smartphone market only has spikes whenever there is a new model out! In the case of the Samsumg Galaxy that spike is in February and in the case of the Apple iPhone that happens in September. Now let's see how the enterprise technology trends compare against each other.


I just realised that "Artificial Intelligence" is not comparable with "Blockchain", because the first one just captures searches in English language. As you can see below "Blockchain" is present across the world, while "Artificial Intelligence" is very language dependent.


"Global Warming" is of course very language dependent. So we need to compare things that are either language neutral or in the same language. And that means comparing "Global Warming" from the science side with "Artificial Intelligence" from the technology side, and then "Biotechnology" from the with "Blockchain". And finally stack up the "winners" from both against each other and finally end this long rant about how Science is not as popular as Technology.

Science wins!

Technology wins!


Technology wins again :)

Don't lose sight of the title of this post. The fact that technology is a result of scientific discovery and technology is applied science is not clear to everyone, in particular the people who depend on technology, which is most of us in the developed world. I say "us" because if you are reading this you have to own a device to access the internet and that puts you right on the half of the world's population who has internet access. Which in itself is also a technology wonder. And that is precisely the point with today's wonder and awe: it's all directed at technology and very few at science. But science in order to move forward needs to use technology in order to process its data. Let's recap what is the "scientific method" and why it relies so much on data it captures from observation.

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There are two important aspects to the scientific method that rely on its source data: data quality, and ability to store the intermediate results from all the experiments. Field science, applied science or even non-theoretical scientists have to come up with ways to speed up the process of testing hypothesis, which in turn will lead to other hypothesis, and this work can take years. It's a complex tree of ramifications of hypothesis and at each new level more data is generated, and the more ramifications and branches it creates, the bigger the number of combinations and the harder it gets to combine and process all this data. But can they all afford to store, analyse and process all that data? In most cases scientists and researchers work with simulation tools that will allow them to speed up the process of analysing the data generated in real time, or at the same time as the data is being generated.
So the frontier for the scientific research that will push technology to the next level is dependent on the data that science is able to process today, which in turn dependes on the technology of today. Unless we have a breakthrough in technology so big, that it will expand the limits of science research.



January 19, 2016

The fallacy of technology

It has been said over and over that technology shapes behavior, and that behavior fuels technology changes. But if you take the magnifying glass and look closer, we'll see that the relationship is far more complex and in some cases quite unpredictable.

In order to make this point clearer let's start with an real life example. Videoconferencing and video calls. This is a true example of a type of technology that used to be marketed some years ago as something that would shape behavior and change the way people communicate. In the business world video conferencing was for a long time a very expensive technology, and one that could only be afforded by companies with centers of decision scattered across the globe, or distant geographical locations. As soon as broadband telecommunications price started dropping and video compression technologies evolved, video conferencing started to be available to more and more companies and organizations. Did this change the way people worked?
Did this revolutionized the remote working practices? Of course not. The chain of command that is enforced in most organizations is still very much based in personal interactions, face to face communication, endless meetings, and non-verbal communication to send power based messages that somehow are diluted in video communication.
Did technology changed behavior in this space? No.

Let's now go on to the personalized aspect of video communication. The mass market adoption. Starting with Skype and then later with Apple's Facetime or Google's Hangouts. Who are the main users of such technology? Well, families can be considered as being in that group. Elements of a family that are either out on a trip, or live distant from each other. Specially families of military serving overseas. Now, not all families that are separated by distance use video. Over the years several attempts have been made to make this a profitable technology, but somehow people got used to the idea that it's there, exists, and it is used in very particular situations, but does not change behavior.

Video based communication is just one small example in the long line of technology that is part of the predictions made in the past about its impact in the future.

On the other hand, internet based social networks have taken off dramatically in the last 10 years. This is a very good example of the dichotomy between technology and behavior. A lot has been said and written about the impact of social networks, but very few people stop and think which one shapes the other.

I guess what I'm trying to say here is that technologies that seem useful contain the fallacy that they will change human behavior, and others that seem superficial and somewhat irrelevant are picked up by human behavior and taken to levels of importance that no one could foresee.

The key element here is time. The next time you read or hear that technology A or B will change the world, please be aware of these considerations, because sometimes irrational behavior shapes technology, and completely shallow and somewhat useless technology will most probably have a dramatic impact on human behavior.

November 19, 2015

Hybrid Cloud is a two way street


A lot has been said about Uber. But probably what few of us know is that the people that drive Uber cars have day jobs: they work at stores, teach in schools and in some countries they also drive cabs as well. This digital platform that is changing the way we move and share car transportation is first and foremost a job enabler. From one end the customer has agility, flexibility and low price, and from the other end, people that want a second or third revenue stream have the opportunity to do it transparently. In the middle there’s the platform. Paul Sonderegger, the Oracle Strategist for Big Data, explains it on his series “Data Capital”. The three main principles: 1) Data is a capital on par with financial capital 2) Data generates more data and 3) Platforms tend to win. And everybody is fighting to be “the platform” that intermediates digital businesses of getting people from point A (here) to point B (there).
Paul's session at the Oracle Open World (OOW) conference in San Francisco, made it abundantly clear that, like financial capital, data capital can be used by organisations to distance themselves from the herd, or simply to offer a better service: public or private.

The choice deployment model is the step that precedes the adoption or creation of a platform to enable the usage of data as capital. And this deployment model, for most companies will be a mix between public cloud services, private cloud services and IT functions that are scattered around the organisations. This deployment model is called: Hybrid Cloud. Mark Evanko, CEO at Bruns Pak, a USA, NJ based Data Center company, calls it a "mix of data center facilities, co-location, cloud services, network and disaster recovery". 

Judith Hurwitz, co-author of the "Hybrid Cloud for Dummies" book, talked about "touch points" between public and private clouds that articulate combined services. She also mentions that these are early days. But that was 2012! And this is 2015 almost 2016!! There are many "touch points" today, and there are several platforms that claim to enable the creation, management and automation of these combined services.

But on this journey, Hybrid Cloud is a two way street. It needs an operational model that caters for both movements: to the cloud and back. The tools are obviously there (Oracle, Microsoft, Red Hat and Google all have big investments in this area), but only one vendor seems to shout out loud that the tech stack is the same on both ends: Oracle. Some organisations have already started a public cloud journey with several vendors. This is an even bigger "mix".

A cross vendor public cloud deployment is also something that has been discussed in several forums, as organisations are doing this movement of evaluating the mix of several public clouds. The IT industry has made this movement in the past, and after an inflexion point it started a consolidation effort without precedent. From consolidation to convergence. The burning question is: will the Hybrid Cloud deployments follow this trend? In hindsight most organisations have already started big consolidation movements. Some of them with Oracle technology. This is why they should continue this journey leveraging hybrid cloud use cases, and holding hands with a strategic cloud partner.  

The challenge remains the same. Mark Hurd's OOW keynote emphasised it clearly: “It’s about getting from here to there, at a lower cost and get fast innovation… now!”